Antibiotic susceptibility testing via plasmonic imaging and tracking

ABSTRACT

A rapid antibiotic susceptibility test (AST) based on the detection and quantification of the movement of single bacterial cells with a plasmonic imaging and tracking (PIT) technology. The PIT-based AST detects changes in the metabolic activity of the bacterial cells long before cell replication, and allows rapid AST for both cultivable and non-cultivable strains. PIT tracks 3D movement with sub-nanometer resolution and millisecond temporal resolution. PIT also allows simultaneous measurement of the binding kinetic constants of antibiotics and bacterial metabolic state after the introduction of antibiotics.

RELATED APPLICATION

This application claims priority from U.S. application No. 62/200,500 ofNongjian Tao et al., filed Aug. 3, 2015, entitled “ANTIBIOTICSUSCEPTIBILITY TESTING VIA PLASMONIC IMAGING AND TRACKING.” U.S.application No. 62/200,500 is hereby incorporated by reference.

TECHNICAL FIELD

The present invention relates to plasmonic imaging. More particularly,the invention relates to using plasmonic imaging and tracking to carryout antibiotic susceptibility testing related to bacterial infections.

BACKGROUND

Antibiotic-resistant bacterial infections, in acute cases like sepsisand others, result in costing the US billions of dollars in healthcarecosts. Estimates vary but have ranged as high as $20 billion in excessdirect healthcare costs, with additional costs to society for lostproductivity as high as $35 billion a year (2008 dollars)¹. Theprevailing view is that the widespread misuse of antibiotics over thelast few decades has allowed bacteria to evolve and develop defenses toneutralize or resist antibiotics. Antibiotic-resistant bacterialinfections are spreading at a phenomenal rate and seriously threatenhuman survival and severely set back medical progress made in the lastcentury².

Today, clinical treatment of bacterial infections, especially in acutecases of sepsis, requires multiple steps including (AST).Conventionally, AST requires time-intensive culturing techniques, suchas disk-diffusion³ and broth-dilution⁴, which can take up to two daysfor the bacteria to grow to an appropriate density for clinicalassessment. In addition to being time consuming, such AST techniques arelimited to cultivable strains of bacteria, leading to delayedadministration of appropriate antibiotics that often results in puttingpatients at risk. Appropriate antibiotic regimens can be unduly delayed,especially for slow-growing and non-cultivable microorganisms. A fasterAST is needed to reduce morbidity and mortality rates significantly.

With an increasing clinical demand for AST, multiple methodologies havebeen developed to characterize antibiotic activity on bacterialmetabolism. Examples include the measurement of incremental increases incell length and number.^(5,8) While these approaches have met with somedegree of success, they still rely on culturing, which is notuniversally applicable, especially to non-cultivable microorganisms andanaerobes, new bacterial strains, and slow-growing bacteria.¹¹

Techniques, such as magnetic beads^(5,6) and optical imaging,^(7,8) havebeen used to measure cell growth by proxy means, recording changes invibrational amplitude or image intensity. While these alternativetechniques meet some requirements, they are still time-consuming andsemi-quantitative since they require the bacteria to be grown to highdensity. More recently, micro-cantilever deflections have been used asmetabolic sensors to detect bacterial cell motion.^(9,10) In the case ofatomic force microscope (AFM) cantilevers, one major disadvantage is thelack of means to differentiate strains and obtain strain-specificsusceptibility results. This cantilever approach would be difficult touse when a patient has a polymicrobial infection.

Thus, for humans to win the evolutionary battle between our wits andmicrobial genes, there is a crucial need for point-of-care technologiesthat can rapidly generate antibiotic susceptibility profiles of aninfecting pathogen, ideally at the earliest stages of disease. Fastgeneration of antibiotic susceptibility profiles would allowadministration of appropriate narrow-spectrum/personalized therapies atthe earliest possible stage. Automated, and more universal technologiesfor antibiotic susceptibility testing (AST), are needed to replacecurrent culture-based approaches. Such a technology would also beapplicable to non-cultivable and slow growing microbial species andconsiderably reduce time required to obtain a susceptibility report.

The present invention overcomes the limitations inherent in the knownmethods described above. Disclosed herein for the first time is an ASTtool based on a plasmonic imaging technology for simultaneous and rapidmeasurement of the binding kinetics and treatment effects of antibioticson bacteria in a culture-free environment. This novel AST method canquickly detect antibiotic resistant strains and improve clinicaldiagnoses.

BRIEF SUMMARY OF THE DISCLOSURE

This summary is provided to introduce, in a simplified form, a selectionof concepts that are further described below in the DetailedDescription. This summary is not intended to identify key features ofthe claimed subject matter, nor is it intended to be used as an aid indetermining the scope of the claimed subject matter.

The present invention provides a method for plasmonic imaging of bindingkinetics of antibodies with bacterial cells. A plasmonic imaging andtracking (PIT) system includes an inverted microscope lens, a lightsource, a metallic coated slide, a mirror and a detector. Tetheringmolecules are attached to the metallic coated surface. An analyte isplaced on the metallic coated surface and the PIT system is activated toimage the analyte. A first set of 3D motion values of the analyte istracked and an antibiotic is subsequently added to the metallic coatedsurface. After adding the antibiotic, a second set of 3D motion valuesof the analyte is tracked. The first and second 3D motion values arecompared to determine changes in the 3D motion of the analyte afteraddition of the antibiotic.

In one aspect, the tethering molecules have an affinity to a bacterialcell under investigation.

In another aspect the tethering molecules comprise antibodies.

In yet another aspect, the analyte is a bacteria selected from the groupconsisting of E. coli and S. aureus.

In yet another aspect, the attaching tethering molecules include anti-E.coli antibodies.

In yet another aspect, adhesion materials are applied to the metalliccoated surface, where the adhesion materials are selected from the groupconsisting of cell-adhesion promoting substances, poly-lysine, and agarmatrix.

In yet another aspect, the computer program for tracking 3D motionvalues tracking the XY-motion comprises a curve fitting algorithm.

In yet another aspect, the curve-fitting algorithm is selected from thegroup consisting of Gaussian fitting, elliptical fitting, and spatialaveraging.

In yet another aspect, the computer program further comprises asubprogram for extracting an image intensity change from the plasmonicimage that is free of noise by transforming the plasmonic image intoK-space using Fourier transforms to produce a two-ring image.

BRIEF DESCRIPTION OF THE DRAWINGS

While the novel features of the invention are set forth withparticularity in the appended claims, the invention, both as toorganization and content, will be better understood and appreciated,along with other objects and features thereof, from the followingdetailed description taken in conjunction with the drawings, in which:

FIG. 1 schematically shows a process flow for plasmonic imaging andtracking (PIT) of bacterial cell metabolic activity-related 3D movementwith nanometer resolution.

FIG. 1A graphically shows comparative measurements using plasmonicimaging and tracking of bacterial cell metabolic activity-related 3Dmovement before and after introduction of an antibiotic.

FIG. 2A-FIG. 2D show images of bacterial z nanomotion revealed by thevarying plasmonic image contrast.

FIG. 2E graphically illustrates Z-Distance between bacterium and plasmonsurface vs time plot with an average nanomotion magnitude of ˜6 nm.

FIG. 2F graphically illustrates Z-Distance between bacterium and plasmonsurface plot of a dead bacterial cell (no motion) showing an averagemotion magnitude of 0.50 nm. Scale bar (2 μm).

FIG. 2G schematically shows a typical trace of mitochondrion movementtracked using PIT.

FIG. 3A-FIG. 3D graphically illustrate how Z-motion changes after addingantibiotics.

FIG. 3E graphically illustrates amplitude analysis of Z-movements indifferent media.

FIG. 4A-FIG. 4D graphically illustrate plasmonic imaging of bindingkinetics of antibodies with individual bacterial cells.

FIG. 5 shows sensorgram signals of single bacterial cells obtained byplotting the image intensity of the colored boxes for bacterial cellsand control with time.

FIG. 6A-FIG. 6B illustrate k-space analysis of PIT images.

FIG. 7A-7F, show Z-movement of individual bacterial cells before andafter antibiotic treatment (FIG. 7A, FIG. 7C and FIG. 7E) paired withPSD of Z-movement of individual bacterial cells before and afterantibiotic treatment (FIG. 7B, FIG. 7D and FIG. 7F).

FIG. 8A presents the amplitude histograms of 31 individual bacterialcells before and after antibiotic treatment.

FIG. 8B shows statistical analysis (Student t test) of the meanamplitude value for all bacterial cells before and after antibiotictreatment.

FIG. 9A-FIG. 9C show Z-movement in 1×PBS and LB medium.

FIG. 10 shows Z-movement in 1×PBS and different concentrations ofantibiotic.

FIG. 11 shows cell death as observed by transmitted images.

FIG. 12 shows nano-motion changes for UPEC strain on APTES surface.

FIG. 13 shows Z-movement for antibiotic Streptomycin for E. coli O157:H7is shown.

In the drawings, identical reference numbers identify similar elementsor components. The sizes and relative positions of elements in thedrawings are not necessarily drawn to scale. For example, the shapes ofvarious elements and angles are not drawn to scale, and some of theseelements are arbitrarily enlarged and positioned to improve drawinglegibility. Further, the particular shapes of the elements as drawn, arenot intended to convey any information regarding the actual shape of theparticular elements, and have been solely selected for ease ofrecognition in the drawings.

DETAILED DESCRIPTION

The following disclosure describes an AST testing regime employingplasmonic imaging and tracking. Several features of methods and systemsin accordance with example embodiments are set forth and described inthe figures. It will be appreciated that methods and systems inaccordance with other example embodiments can include additionalprocedures or features different than those shown in the figures.Example embodiments are described herein with respect to measuring 3Dmotion of bacterial cells. However, it will be understood that theseexamples are for the purpose of illustrating the principles, and thatthe invention is not so limited.

Unless the context requires otherwise, throughout the specification andclaims which follow, the word “comprise” and variations thereof, suchas, “comprises” and “comprising” are to be construed in an open,inclusive sense that is as “including, but not limited to.”

Reference throughout this specification to “one example” or “an exampleembodiment,” “one embodiment,” “an embodiment” or combinations and/orvariations of these terms means that a particular feature, structure orcharacteristic described in connection with the embodiment is includedin at least one embodiment of the present disclosure. Thus, theappearances of the phrases “in one embodiment” or “in an embodiment” invarious places throughout this specification are not necessarily allreferring to the same embodiment. Furthermore, the particular features,structures, or characteristics may be combined in any suitable manner inone or more embodiments.

Definitions

Generally, as used herein, the following terms have the followingmeanings when used within the context of plasmonic imaging:

As used herein “amplitude” is defined as the standard deviation of theZ-movement for a given period of time.

“AST” as used herein generally refers to antibiotic susceptibilitytesting. “3D movement” as used herein refers to metabolicactivity-related 3D movement.

“PIT” as used herein refers to plasmonic imaging and trackingtechnology.

As used herein “Z-movement” refers to the relative Z-distance, where themean bacterium-plasmon surface Z-distance in a given time duration issubtracted from the Z-distance at a given period of time.

Using the methods and systems described herein metabolic activity can bequantified by measuring and analyzing the 3D movement in real time withnanometer resolution. In this way active metabolic movement of a livingcell can be distinguished from that of a dead cell, for example. Theapproach is supported by a recent observation of bacterialmetabolism-induced fluctuations by AFM.^(9,12) Using plasmonic-basedmicroscopy, simultaneous imaging of mass distribution, electricalimpedance, and molecular binding kinetics of single bacterial cells havebeen demonstrated.¹³ Additionally, it has been shown that it is possibleto track the 3D movement of single bacterial cells and organelles in 3Dwith <5 nm spatial resolution and <1 ms temporal resolution. PIT cananalyze multiple bacterial cells simultaneously, providing highthroughput and quantification of AST with single cell detection and witha capability for statistical analysis of multiple cells.

Compared to traditional culture-based AST techniques, the instant PITapproach monitors 3D movement associated with metabolic activity ofbacteria, which is more universal, and can detect metabolic changes dueto external metabolites long before cells actually replicate. Thisstrategy enables real-time detection of viable antibiotic-resistantstrains (with negligible changes in metabolic activity from patient tolaboratory) and offers a significant advantage over the culturingtechniques. This approach could be developed into a rapid clinicaldiagnostic tool (<1 h). One important advantage of PIT over AFMcantilever is the ability to characterize AST on single cells in a mixedbacterial population. In addition, PIT can spatially resolve bacterialcells even in a complex matrix of sera, body fluid samples, etc. Thisadvantage is very critical in translating this technology into apractical solution for testing real patient samples.

Another important innovation is disclosed for simultaneously measuringthe binding kinetics of antibiotics to a single bacterial cell and thecellular metabolic effect of antibiotic binding. Previously, surfaceplasmon resonance has been used to measure only the binding kinetics ofmolecules to a large number of cells immobilized on a surface.Considering the large cell-cell evolutionary heterogeneity in amicrobial population and the importance of the heterogeneity inantibiotic resistance, measuring kinetic parameters for bulk populationlosses valuable information.

Antibody binding kinetics of single bacterial cells have beenmeasured,¹³ and four orders of magnitude cell-to-cell variability hasbeen observed in the equilibrium constant, K_(D), for agenetically-similar cellular population. Thus, inherent variability ofthe microbial surface has been demonstrated within a given population.This inherent variability is considered to be even more critical whenassessing antibiotic action, which have downstream effects on cellularmetabolism, structure, and/or replication. Hence, measuring single cellK_(D), followed by extrapolation to a bulk population, is considerablymore relevant in assessing antimicrobial efficacy. By correlatingmetabolic activity from 3D bacterial movement and antibiotic bindingkinetics, the kinetic and MIC measurements of antibiotics can becombined into a single test using PIT.

To summarize, PIT enables:

-   -   a) Rapid and automated AST (<1 h) of antibiotic-resistant        bacteria.    -   b) Universal approach to detect both cultivable and        non-cultivable bacteria.    -   c) Kinetics and MIC measurements of antibiotics in a single        step.    -   d) Single bacterial cell MIC curves in a mixed microbial        population as well as complex samples of bacteria in sera, body        fluids etc. using imaging capabilities.

Referring now to FIG. 1, a process flow for plasmonic imaging andtracking (PIT) of bacterial cell metabolic activity-related 3D movementwith nanometer resolution is schematically shown. A plasmonic imagingand tracking (PIT) system 10 includes an inverted microscope lens 12, alight source 14, a metallic coated slide 16, a mirror and a detector 18.Tethering molecules 20 are attached to the surface of slide 16. Thetethering molecules 20 are selected to tether the analyte 22 to thesurface. The analyte may comprise a bacterial cell under investigation.In some cases, depending on the affinity of the analyte, antibodies areused to tether the analyte as described below.

A processor 5, such as a computer processor of any suitable type,contains software programs 7 for processing images obtained by the PITsystem 10 and for controlling the operation of the PIT system 10. Imageprocessing and quantitative measurements are carried out by algorithmsembodied in the software programs as described in more detailhereinbelow.

In one example, imaging and cantilever detection of metabolicfluctuations have been combined using the plasmonic imaging and tracking(PIT) system to track bacterial cell 3D movement associated withmetabolic activity and measure the binding kinetics of antibiotics.Clinically-relevant bacterial strains, such as E. coli O157:H7 (Gramnegative, motile) and S. aureus (Gram positive, non-motile), were usedto demonstrate the PIT-based AST technology. Plasmonic resonance at theslide surface causes the analyte to move with 3D motion in the X, Y andz planes as referenced to a Cartesian coordinate system 30. The 3Dmotion is measured before and after the addition of an antibiotic 40.Panel 42 schematically illustrates a reduction in Z-motion afterintroduction of an antibiotic into the system.

A crucial task for PIT-based AST is to create a robust protocol totether bacterial cells onto the sensor chip (gold-coated glass slides).The cells must be close to the sensor surface, within a distance of afew hundred nm for optimal PIT sensitivity, and yet the attachment ofthe cells to the surface cannot be too strong so as not to significantlyhinder the 3D movement. In one example, anti-E. coli O157:H7 antibodieswere used to non-covalently attach bacterial cells. This protocol allowsfor observation of 3D movement in metabolically active cells that ismuch greater than Brownian motion (dead cells). Other tethering schemes,including cell-adhesion promoting substances, poly-lysine, and agarmatrix,²⁰ for a mixed population of Gram positive and Gram negativebacteria can also be used.

In one useful embodiment a PIT setup can be built on an opticalmicroscope with a high numerical aperture objective.⁶²⁻⁶⁴ A sensor chip,made of a glass coverslip coated with 47 nm thick gold film, is placedon the microscope sample stage. Light with a wavelength of 680 nm from asuper luminescent diode is directed onto the film via the objective.When the incident angle is tuned, surface plasmons are excited on thegold surface, and the reflected light is imaged with a CCD imager. TheE. coli O157:H7 cells, tethered on a sensor chip via antibody coupling,scatter the surface plasmonic wave, leading to parabolic-shaped patternsin the plasmonic image.⁵⁷

Referring now to FIG. 1A, comparative measurements using plasmonicimaging and tracking of bacterial cell metabolic activity-related 3Dmovement before and after introduction of an antibiotic is graphicallyshown. Frame 50 is a graphical representation of Z-motion (nm) on thevertical axis and time in relative units on the horizontal axis. Frame50 is split into identically scaled left 52 and right 54 temporalframes. The left temporal frame 52 graphically maps Z-motion of ananalyte before introduction of an antibiotic. Conversely, right temporalframe 54 graphically maps Z-motion of an analyte after introduction ofan antibiotic into the same analyte. A visual comparison of the rightand left frames reveals a significant reduction of Z-motion afteraddition of the antibiotic.

Referring now concurrently to FIG. 2A-FIG. 2D, images of bacterial znanomotion revealed by the varying plasmonic image contrast are shown.The images shown were obtained using 1×PBS as the medium. To ensure thatthe bacterial cells attached to the surface are metabolically active,the buffer from was changed from 1×PBS to Luria Broth (LB) culturemedium and the cells were incubated for approximately 20 min. Afterincubation in LB, bacterial cells grew, as evident by the elongation ofcells detected in the transmission images. Comparisons of the nanomotionof live bacterial cells in 1×PBS and LB revealed similar amplitudes(FIG. 9A).

When analyzed, the motion of the bacterial cells in the transmission andthe plasmonic images showed that the transmission image contrast of thebacterial cells appears to be constant, but the plasmonic image contrastfluctuates significantly. To show the contrast fluctuation of theplasmonic image, differential plasmonic images were created bysubtracting the lowest contrast image from all of the images. Thedifferential images shown in FIG. 2A-FIG. 2D reveal large fluctuationsin the plasmonic image contrast of a bacterial cell at times t=1 s, t=5s, t=10 s and t=15 s respectively. This image contrast fluctuation isdue to the nanomotion of the bacterial cell normal to the z sensor chip(z direction),⁶⁵ which is due to the surface plasmon intense evanescentelectric field, which decays exponentially from the surface into thebulk solution. Consequently, the scattering of the plasmonic waves bythe bacterial cell decreases exponentially with the distance (z) betweenthe cell and the sensor surface. It has been shown previously that theplasmonic image contrast change (Δl/l) of a particle is related to thedistance change (Δz), by Δl/l=exp(−Δz/95.8 nm).⁶⁶

Referring now to FIG. 2E, Z-Distance between bacterium and plasmonsurface vs time plot with an average nanomotion magnitude of ˜6 nm isgraphically shown where the scale bar represents 2 μm. Using thisrelation, the nanomotion of the bacterial cell in z direction can bedetermined. The magnitude of the nanomotion of the bacterial cell abovethe plasmon surface, is less than 10 nm with an average motion magnitudeof ˜6 nm, which cannot be detected in the traditional transmissionoptical image.

Referring now to FIG. 2F, Z-Distance between bacterium and plasmonsurface plot of a dead bacterial cell (no motion) showing an averagemotion magnitude of 0.50 nm is graphically shown where the scale barrepresents 2 μm. As a control experiment, the nanomotion of a deadbacterial cell was tracked and observed a much smaller magnitude ofnanomotion (˜0.50 nm). The data demonstrate the capability of theplasmonic imaging technology for tracking the motion of individualbacterial cells with sub-nanometer precision.

As supported by further evidence shown below, the bacterial nanomotionis related to the bacterial metabolism. For live cells, bacterialmetabolism is associated with cytoplasmic membrane transport,⁵²cytoplasm fluidity,⁵⁷ and modifications of membrane lipid composition inresponse to environmental changes,⁶⁸ all of which can cause micromotionof the cells. In the present system, the bacterial cells are attached tothe surface via antibodies, involving soft noncovalent bonds thatprevent large-scale motions, but allow nanomotion of the bacterialcells.

EXAMPLE EMBODIMENTS

Tracking Mitochondrion Movement with PIT

Referring now to FIG. 2G, a typical trace of mitochondrion movement intime-lapse plasmonic images is graphically illustrated. A representativetrace 200 of mitochondrion movement is tracked using PIT. X and Ytranslation within 5 nm can be resolved on the horizontal axes, and Zmovement is reflected by the image intensity (expressed in a.u.) alongthe vertical axis. For 3D motion tracking with PIT, a PIT setup on aninverted optical microscope enabling simultaneous capture of plasmonicand impedance images along with additional transmitted, fluorescenceTIRF images for validation and additional experiments.¹⁵⁻¹⁸ Using thissetup, organelles moving inside a cell with 5 nm spatial resolution and<1 millisecond temporal resolution have been tracked successfully. Byfitting the spatial distribution of the plasmonic image intensity with aGaussian function, a moving organelle in both X and Y directions can beresolved, noting that the movement in z direction is reflected by theplasmonic image intensity (additional details provided below).

3D Movement of Individual Viable and Dead Bacterial Cells

Example 1

Referring now concurrently to FIG. 3A-FIG. 3B, Z-motion changes afteradding antibiotics are graphically illustrated. In these plots, thevertical axis represents Z-motion in nanometers (nm) and the horizontalaxis represents time in seconds (s). Referring now specifically to FIG.3A, Z-motion of a bacterial cell before and after adding polymyxin B att=120 s is shown. The large drop in Z-motion associated with the addingof polymyxin B, confirms the feasibility of the invention.

Referring now specifically to FIG. 3B, Z-motion of a background controlregion, which shows no Z-motion changes after addition of the antibioticis shown. Z-direction movement tracking is based on the exponentialdependence of the plasmonic intensity of an object on its Z movement.¹⁹Using the procedures and systems disclosed herein, the tracking accuracyin z direction can reach as high as 0.1 nm.¹⁹ To demonstrate this PITcapability for studying bacteria movement, the movement of a livingbacterial cell in z direction with concurrent addition of theantibiotic, polymyxin B, at a bactericidal concentration of 75 μg/ml wastracked. Before the addition of polymyxin B, Z-movement decreases from±5 nm to ±0.5 nm, which is close to the Brownian motion of a deadbacterium. We further validated the metabolic state by culturing andelongating the tethered cell in standard Luria Broth (LB) media beforeadding the antibiotic. After introducing polymyxin B, subsequentincubation of the bacterium in LB media did not lead to cellular growth,confirming effective antibiotic-mediated killing. This preliminaryexperiment demonstrates the feasibility of PIT for rapid antibioticsusceptibility testing.

Referring now to FIG. 3C the effect of antibiotic polymycin B at aconcentration of 500 μg/mL on Z-movement of a bacterial cell is shown.We then studied the effects of antibiotics on the nanomotion of livebacterial cells by adding polymyxin B (PMB) to 1×PBS to reach a finalconcentration of 500 g/mL. PMB is a bactericidal antibiotic which killsGram-negative bacteria by permeabilizing the outer membrane.⁶⁰ At highconcentrations, such as 500 g/mL used in this study, PMB alsodepolarizes the cytoplasmic membrane, causing ion-permeable pores in thecell envelope.³⁰ Within a few seconds after adding the antibiotic, therewas a marked decrease in the Z-movement of the bacterial cell from about±3 to about ±1 nm. We further observed that after a few minutes, thebacterial cell Z-movement reached a baseline value of ±0.5 nm. Thisdecrease in bacterial nanomotion can be attributed to bactericidalactivity of PMB, which has been observed at a concentration of 20g/mL.30 We used 25× for the bactericidal concentration in this projectto ensure complete loss of cellular viability. Bactericidal activity wasalso observed by comparing cellular morphology after adding antibiotics,where decreases in bacterial cell length at a high antibioticconcentration were visualized (FIG. 10). The decrease in bacterial celllength has been previously correlated to cell lysis and cell death afterthe addition of polypeptide antibiotics.⁶⁹ We validated the correlationbetween the decreases in nanomotion and change of the bacterialmetabolic state by replacing the antibiotic-containing PBS with LBmedium (lacking PMB) on the sensor chip. We observed no further changein the bacterial nanomotion after incubating in LB medium, indicatingirreversible loss of metabolic activity after the treatment with PMB. Wesubsequently collected a small sample volume from the above sensor chipand subjected it to culturing overnight in LB medium. We observed nogrowth of bacterial cells after overnight incubation, thus confirmingbacterial cell death and bactericidal activity of the PMB antibiotic.

Furthermore, we injected antibiotic with a sub-bactericidalconcentration (0.5 g/mL), followed by a 5× bactericidal concentrationinjection. At sub-bactericidal concentrations, we did not observechanges in the nanomotion after 20 min. However, after injectingbactericidal concentrations we observed a significant decrease innanomotion, which validates the correlation between the decrease innanomotion and change of the bacterial metabolic state. (FIG. 10). Weperformed a further control experiment by injecting glucose into thePBS-bacteria mixture. Glucose is a chemo-attractant and represents anenergy source for the bacteria.

Referring now specifically to FIG. 3D, there shown are controlexperiment data comparing Z-movement in PBS 102 and on injections of 2%glucose 104, 1×PBS 106, and antibiotic 108. After the injection of 2%glucose, the nanomotion of the bacteria increased slightly. Aftersubsequently eliminating glucose in the 1×PBS medium, the amplitude ofthe nanomotion, measured over 1 s windows for 20 s videos, decreasedback to the level prior to the injection of glucose (as shown in FIG.3E). The positive correlation between glucose injection and Z-movementstrongly supports that bacterial cell nanomotion originates frommetabolic activity rather than Brownian motion. Conversely, we observeda decrease in bacterial nanomotion only when PMB was added at thebactericidal concentration of 75 g/mL, thus indicating that the decreasein nanomotion is specific to antibiotic action. Antibiotic activity wasalso observed by the transmitted microscope image, which shows a visibledecrease in cell length after the addition of PMB. We subsequentlysubjected the experimental sample to overnight culturing in LB mediumand observed no bacterial growth, thus validating PMB-induced cell death(FIG. 11).

Binding Kinetics of Antibodies with Single Bacterial Cells

Referring now to FIG. 4, FIG. 4A-FIG. 4D graphically illustrateplasmonic imaging of binding kinetics of antibodies with individualbacterial cells on a scale of 2μ. Referring specifically to FIG. 4A, abright-field optical image of tethered E. coli O157:H7 cells is shown.Referring specifically to FIG. 4B and FIG. 4C, time-differentialplasmonic images captured during different stages of associationprocesses are shown. Referring specifically to FIG. 4D, atime-differential plasmonic image captured during the dissociationprocess is shown. Within each of FIG. 4A-FIG. 4D specific boxed regions70, 72, 73, 74 and 80 have been marked. Region 80 is a backgroundcontrol region. The other regions are mapped at various times as shownwith reference to FIG. 5 as described below.

Referring now to FIG. 5 sensorgram signals of single bacterial cellsobtained by plotting the image intensity of the delineated boxes forbacterial cells and control with time are shown. The sensorgram signalsare referenced as 70A, 72A, 73A, 74A and 80A where the leading numeralscorrespond to regions 70, 72, 73, 74 and 80 respectively. Using PIT,binding of antibody (Ab157) to single E. coli O157:H7 cells wasimaged.¹³ 1×Phosphate Buffered Saline (1×PBS) was flowed over thebacteria for 2 min to record the baseline plasmonic signal, and then1×PBS containing 10 μg/ml of Ab157 to observe the antibody associationphase. We imaged the binding for 3 min to image the dissociation phase,and then passaged again with 1×PBS. Snapshots of the plasmonic imagesshow a weak contrast in the bacterial region at t=0 s (FIG. 4B), whichis attributed to the slight 3D movement of live cells. Snapshots att=180 s and 360 s (FIGS. 4C and 4D) show an increase in the imagecontrast of the bacterial cells compared to the background, which is dueto the binding of antibody specifically to the bacterial cells. A smallimage intensity increase in the background control region occurs att=180 s (FIG. 4C, box 80), which is attributed to bulk refractive indexchange caused by the change of solution. The images also showdifferential contrast increase for different cells, demonstrating thecell-to-cell heterogeneity that is washed out in the bulk assay.

Still referring to FIG. 5, the sensorgrams graph image intensity vs.time profiles provide quantitative kinetic information (k_(a), k_(d),and K_(D)) of the antibody binding to bacterial cells. Note that thekinetic constants can vary over 4 orders of magnitude,¹³ indicating thenatural phenotypic diversity in a bacterial population. Superimposed onthe sensorgrams is “noise” associated with 3D movement, demonstrating acapability of simultaneous binding kinetics and movement analysis ofPIT. The present invention uses an algorithm to analyze and quantifyboth parameters.

An algorithm, typically structured and operated as a computer program,is advantageously used to quantify active metabolism-induced 3D movementof single cells in real time. The algorithm balances the need ofreliability, accuracy, and speed based on the actual imaging quality andoperates to track XY-motion and Z-motion. Since the plasmonic image of abacterial cell is a bright spot with parabolic shape tail due to thescattering of surface plasmons by the cell, the XY-motion of the cell istracked by detecting the bright spot at the vertex of the parabola witha curve fitting algorithm, such as Gaussian fitting, elliptical fitting,or spatial averaging to find the position of the bacterium.

In the Z-direction (normal to the sensor surface), the trackingprinciple relies on the sensitive dependence of the plasmonic imagingintensity on the distance between the cell and sensor surface, which isdistinctly different from the XY motion tracking. A second computer-runalgorithm operates to extract the image intensity change that is immuneof noise. One such useful algorithm transforms the image into k-space(or Fourier space), which is a two ring pattern 90 as shown in FIG. 6B.The overall intensity of the rings in k-space is immune to spatialnoises.

Generation of image processing tools allows fast and efficient 3Dtracking of multiple bacterial cells simultaneously. A possiblechallenge of this task is to identify two or more bacterial cells thatare physically close to each other. One approach is to distinguish suchevents from uncorrelated 3D movement. In other words, two closely spacedcells would have an uncorrelated 3D movement. Another solution to thisproblem is to optimize the number of bacterial cells on the sensorsurface to minimize the likelihood of such events. It is believed thatas long as such events are rare, they will not affect conclusion of AST.

Referring now to FIG. 6A-FIG. 6B, k-space analysis of PIT images isillustrated. Specifically, FIG. 6A shows a PIT image of the bacteriavisible as V-shaped patterns in real space. FIG. 6B shows acorresponding PIT image in k-space presenting two distinct rings 60, 62,from which the image intensity of bacterial cells can be extracted.

Referring now to FIG. 7A-7F, Z-movement of individual bacterial cellsbefore and after antibiotic treatment (FIG. 7A, FIG. 7C and FIG. 7E) andPSD of Z-movement of individual bacterial cells before and afterantibiotic treatment (FIG. 7B, FIG. 7D and FIG. 7F) is shown.

Referring specifically to FIG. 7A, FIG. 7C and FIG. 7E, additionalexamples of the nanomotion of several bacterial cells before and afterantibiotic injection are shown, revealing similar reductions in thenanomotion caused by PMB. FIG. 7A shows an example with bacterium 1.FIG. 7C shows an example with bacterium 15. FIG. 7E shows an examplewith bacterium 25. The magnitude of the nanomotion varies from cell tocell, both before and after the antibiotic injection. We attribute thesevariations to bacterial metabolic activities or interactions ofindividual bacterial cells with antibodies present on the sensor chip.

Referring now concurrently to FIG. 7B, FIG. 7D and FIG. 7F, we performedpower spectral density (PSD) analysis on the nanomotion. Referringparticularly to FIG. 7B, for antibiotic-treated bacterial cells wherethe Z-movement is small, the PSD shows weak frequency dependence similarto the background noise shown in the lower trace 110. In contrast, thePSD of the live bacterial cells, shown in the upper trace 112, issignificantly larger and depends on frequency according to 1/f afrequency with 1<a<2 for frequencies between 1 and 50 Hz. Below 1 Hz,the PSD is not reliable because of the mechanical drift of the opticalsystem, and above 50 Hz, the Z-movement decreases to the backgroundnoise level. This PSD feature is similar to that of AFM cantileverdefection associated with multiple bacterial cells attached to thecantilever.⁵⁴

Referring now to FIG. 8A, the amplitude histograms of 31 individualbacterial cells before and after antibiotic treatment are presented. Theamplitude has been calculated as the standard deviation of theZ-movement for a period of 20 s. Before antibiotic treatment, there wasa large variation in the amplitudes of the nanomotion of the bacterialcells. A few cells exhibited low amplitude nanomotion before antibioticaddition and appeared to be tethered strongly to the surface. Afterantibiotic treatment, the amplitude of nanomotion for almost all of thebacterial cells decreased. However, for those cells with low initialamplitudes, the decreases in nanomotion after antibiotic treatment aresmall, which are understandable since the strong tethering restrictsmotion.

Referring now to FIG. 8B, statistical analysis (Student t test) revealedthat the mean amplitude value for all bacterial cells is significantlydifferent before and after antibiotic treatment (p=2.66×10−6).

We further demonstrated the applicability of this technology to UTIinfections by extending it to other clinically relevant strains. Weimmobilized the UPEC strain on the sensor using the(3-aminopropyl)-triethoxysilane (APTES) linker, rather than using theantibody immobilization for the E. coli O157:H7 study described above.We studied the nanomotion changes as antibiotic PMB acted on bacterialcells at a bactericidal concentration of 1 mg/mL. We observed a decreasein bacterial nanomotion when PMB was added indicating that the decreasein nanomotion is specific to antibiotic action. Subsequent overnightculturing of experimental sample in LB medium led to no bacterialgrowth, thus validating PMB-induced cell death (FIG. 12). We alsoapplied the plasmonic tracking and imaging technology to anotherantibiotic, streptomycin, and observed decrease in the nanomotion in thebacterial cells in PBS supplemented with LB media, after addingstreptomycin at a bactericidal concentration of 1 mg/mL24,40 (FIG. 12).These experiments indicate that the methods may be used for differentbacterial strains and different antibiotics. To fully establish thepresent method for rapid AST in clinical settings, additional and morecomprehensive tests will still be needed in the future.

Referring now concurrently to FIG. 9A-FIG. 9C, Z-movement in 1×PBS andLB medium is shown. Specifically referring to FIG. 9A, the left handtrace shows Z-movement of a typical bacterial cell in 1×PBS buffer. Theright hand trace shows Z-movement of a typical bacterial cell incubatedin LB medium. The Z-movements in both 1×PBS and LB medium are comparablewith amplitudes (standard deviation of Z-movement) of 2.31 nm and 2.22nm, respectively. Pink traces show that detectable motion in thebacterial-free area (system noise) are negligible in both cases. FIG. 9Bshows a transmitted image of a bacterial cell in 1×PBS with a recordedlength of 4.81 μm. Scale bar (1 μm). FIG. 9C shows a transmitted imageof a bacterial cell in LB medium with a recorded length of 5.06 μm. Thescale bar for FIG. 9B and FIG. 9C is 1 μm.

Referring now to FIG. 10, Z-movement in 1×PBS and differentconcentrations of antibiotic is shown. The left trace shows Z-movementof a E. coli O157:H7 cell on the sensor chip in 1×PBS buffer. The middletrace shows that introducing sub-bactericidal concentrations (0.5 μg/ml)of antibiotic PMB does not change the Z-movement. However, introducingthe antibiotic at 5× bactericidal concentration (100 μg/m) leads to alarge decrease in Z-movement as shown by the right-most trace.

Referring now to FIG. 11 cell death as observed by transmitted images isshown. Bacterial cell 1 before (a1) and after adding antibiotics (a2).The cell length shrinks after adding antibiotics, which indicatespossible cell death. Bacterial cell 2 before (b1) and after addingantibiotics (b2). The cell length shrinks after adding antibiotics,which indicates possible cell death. Subsequent culturing experimentsconfirmed the death of these cells. Scale bar (1 μm).

Referring now to FIG. 12, nano-motion changes for UPEC strain on APTESsurface is shown. The left trace shows Z-movement of a bacterial cell ofUPEC strain immobilized with APTES on a sensor chip in 1×PBS buffer. Theright trace shows Z-movement after injecting 1 mg/ml polymycin Bantibiotic. The large decrease in the nano-motion is due to bactericidalaction of the antibiotic and subsequent over-night culturing experimentsconfirmed the death of the bacteria.

Referring now to FIG. 13, Z-movement for antibiotic Streptomycin for E.coli O157:H7 is shown. The left trace shows Z-movement of a bacterialcell in 0.4×LB media. The right trace shows Z-movement after injecting 1mg/ml streptomycin antibiotic. The decrease in in nano-motion(Z-movement) correlates to bactericidal action of antibiotic andsubsequent over-night culturing experiments confirmed the death of thebacteria.

Methods

Materials.

Lyophilized bacterial pellets of E. coli O157:H7 (ATCC 43888) werepurchased from Fisher Scientific. UPEC E. coli strain CFT073 waspurchased from ATCC. Affinity-purified goat anti-E. coli O157:H7 IgGpolyclonal antibodies were purchased from Kirkegaard and PerryLaboratory, Inc. (Gaithersburg, Md.), suspended in 1 mL of PBS (1×), andstored in aliquots at −20° C. Polymyxin B (PMB) was purchased fromSigma-Aldrich and dissolved in 1×PBS at a stock concentration of 10mg/mL. PMB was stored in dark at 2-8° C. according to manufacturer'sinstruction. 1-Mercapto-11-undecyl hexa(ethylene glycol) (PEG) andcarboxyl-terminated hexa(ethylene glycol) undecanethiol (PEG-COOH) werepurchased from Nano-science Instruments (Phoenix, Ariz.). Other reagentswere purchased from Sigma-Aldrich.

Preparation and Growth of Bacteria.

The lyophilized bacteria were suspended in PBS centrifuged at the speedof 50 g for 1 min to pellet the charcoal. The supernatant containingbacteria was collected and centrifuged at 2000 g for 15 min to pelletthe bacteria. The bacterial pellet was resuspended in 1 mL of 1×PBS andmixed thoroughly. The final 1 mL of bacteria in PBS solution, after 3rounds of purification, was collected in small aliquots of 20 μL andfrozen at −80° C. adding 5% glycerol. Similarly, E. coli strain CFT073strain was mixed 5% glycerol and frozen in smaller aliquots at −80° C.

An aliquot of frozen E. coli O157:H7 or E. coli CFT073 strain was thawedand used to inoculate 10 mL of LB medium. E. coli O157:H7 cultures wereprepared by diluting the overnight culture (grown at 37° C.) into freshLB medium to a concentration of 107 colony forming units (cfu)/ml andcontinuing growth at 37° C. with gentle rotary mixing until the culturesreached mid logarithmic phase of growth. Bacterial cells were collectedby centrifugation at 2000 g for 15 min and resuspended in 1 mL of PBS(1×).

Surface Functionalization.

Clean BK7 glass coverslips were coated with 1.5 nm chromium and 47 nmgold and used as SPR sensing chips. The chips were cleaned withdeionized water and ethanol for a few times, dried with nitrogen gas,and then cleaned by hydrogen flame. For antibody surface, the cleanedchips were submerged in 1 mM PEG/PEG-COOH ethanol solution and left inthe dark for 24 h to coat a PEG/PEG-COOH self-assembled monolayer (SAM)on each chip. For APTES surface, the cleaned chips were submerged in 1mM PEG solution and left in the dark for 24 h to coat a PEG SAM on thechips. The coated chips were then cleaned again with washes in deionizedwater and ethanol and subsequently dried with nitrogen gas.

To attach antibodies next, the PEG/PEG-COOH SAM-coated chips wereactivated with 500 μL of a freshly prepared mixture of 0.1 M NHS and 0.4M EDC in 1:1 ratio to produce NHS ester receptors, which react with theprimary amine groups on the antibodies via an amide bond. Chips withactivated PEG/PEG-COOH SAM were cleaned with deionized water and blowndry with nitrogen gas. Polyclonal anti-E. coli O157:H7 IgG antibodiesdissolved in 20 mM sodium acetate (NaOAc), pH 5.5 (30 μg/mL), wereimmediately applied to the NHS/EDC-activated surfaces and incubated for60-90 min. The antibody-coated chips were again cleaned with deionizedwater and dried with nitrogen gas prior bacterial cell capture on thePIT setup.

To attach the APTES linker to the sensor surface, the PEG SAM-coatedsensors were activated with 100 μL of a freshly prepared 1% APTES in inethanol (with 5% water) for 2 min. The APTES linked sensor chips wereagain cleaned with deionized water and dried with nitrogen gas priorbacterial cell capture on the PIT setup.

Plasmonic Imaging and Flow Setup.

The plasmonic imaging setup is based on the Kretschmann configurationwith a high numerical aperture objective (NA 1.49) and an invertedmicroscope (Olympus IX-81) (FIG. 1).32-34 The sensor chip was placed onthe objective lens with refractive index matching immersion oil. A 680nm super luminescent diode (Qphotonics, Ann Arbor, Mich.) was used toexcite the SPR images, and a CCD camera (Pike-032B, Allied VisionTechnologies, Newbuyport, Mass.) was used to record PIT images.

A FlexiPerm reusable well (SARSTEDT) was mounted on top of theantibody-functionalized gold chip and filled with 500 μL of PBS (1×)buffer. The assembled gold chip was then mounted on top of the plasmonicimaging setup. The incident angle of the light beam was adjusted to thesurface plasmon resonance angle, revealing minimal image intensity.

Bacterial Immobilization.

Bacterial cells (20 μL) were added to the sensor chip and tethered ontothe sensor surface via noncovalent antibody binding. After a 10-15 minincubation at room temperature, bacterial cells were sufficientlyattached to the gold chip. PBS buffer was subsequently flowed over thechip to remove unattached bacterial cells.

Image Collection and Processing.

All plasmonic imaging sequences were collected at 106 fps at a pixelresolution of 640×480. We chose an appropriate exposure time to maximizeimage intensity and avoid over exposure. Images were recorded in eithertransmitted or plasmonic imaging mode for various time durations.

Sample Addition.

Multiple sample solutions, including LB medium or PBS, were added to thebacterial cells via a gravity-based multichannel drug perfusion system(Warner Instrument, Hamden, Conn.). The drug perfusion system flewsample solutions over the immobilized bacterial cells at a flow rate of330 μL/min with the transition time between different flow solutions inthe range of 1-2 s. The flow system was stopped and stabilized for 5 minbefore adding PMB, streptomycin or glucose. To deliver antibiotics, wepipetted small volumes of antibiotics into the Flexiperm well mounted onthe microscope.

Data Analyses from Images.

We chose small time durations of about 20 s from the videos to analyzeour data and to avoid the influence of focus drift on nanomotionanalysis. The images were processed using custom-written MATLAB programsand ImageJ scripts.

Bacterium-Plasmon Surface Z-Distance Tracking and Z-MovementCalculation.

The plasmonic imaging intensity was calculated by obtaining averagedintensity within a fixed area around the bacterial cell, using the baregold chip regions as a background reference. The Z-distance of thebacterial cell above the plasmon surface was calculated from plasmonicimage intensity with the equation: I_(Δz)=I₀ expΔz/λ.

More specifically, the plasmonic image intensity was calculated byobtaining averaged intensity within a fixed area around the bacterialcell using the bare gold chip regions as reference. The Z-distance ofthe bacterial cell above the sensor surface was then calculated from theplasmonic image intensity (Iz) with a calibrated curve, given byI _(z) =I ₀ exp(−Δz/L),where I₀ is a constant, Δz is the z displacement of a bacterial cell,and L is the decay constant. The decay constant was determined to be˜95.8 nm.35 Using the above calibration, we calculated the error in theZ-displacement to be about 0.1 nm.

The invention has been described herein in considerable detail in orderto comply with the Patent Statutes and to provide those skilled in theart with the information needed to apply the novel principles of thepresent invention, and to construct and use such exemplary andspecialized components as are required. However, it is to be understoodthat the invention may be carried out by different equipment, anddevices, and that various modifications, both as to the equipmentdetails and operating procedures, may be accomplished without departingfrom the true spirit and scope of the present invention.

REFERENCES

The teachings of the following publications are incorporated herein intheir entirety by this reference.

-   1. States, U. Antibiotic resistance threats. (2013). at    <cdc.gov/drugresistance/threat-report-2013/pdf/ar-threats-2013-508.pdf>-   2. Hancock, R. E. W. The end of an era? Nat. Rev. Drug Discov. 6,    28-28 (2006).-   3. Jorgensen, J. H. & Ferraro, M. J. Antimicrobial susceptibility    testing: a review of general principles and contemporary practices.    Clin. Infect. Dis. 49, 1749-55 (2009).-   4. Wiegand, I., Hilpert, K. & Hancock, R. E. W. Agar and broth    dilution methods to determine the minimal inhibitory concentration    (MIC) of antimicrobial substances. Nat. Protoc. 3, 163-75 (2008).-   5. Kinnunen, P. et al. Monitoring the growth and drug susceptibility    of individual bacteria using asynchronous magnetic bead rotation    sensors. Biosens. Bioelectron. 26, 2751-5 (2011).-   6. Sinn, I. et al. Asynchronous magnetic bead rotation    microviscometer for rapid, sensitive, and label-free studies of    bacterial growth and drug sensitivity. Anal. Chem. 84, 5250-6    (2012).-   7. Price, C. S., Kon, S. E. & Metzger, S. Rapid antibiotic    susceptibility phenotypic characterization of Staphylococcus aureus    using automated microscopy of small numbers of cells. J. Microbiol.    Methods 98, 50-8 (2014).-   8. Fredborg, M. et al. Real-time optical antimicrobial    susceptibility testing. J. Clin. Microbiol. 51, 2047-53 (2013).-   9. Longo, G. et al. Rapid detection of bacterial resistance to    antibiotics using AFM cantilevers as nanomechanical sensors. Nat.    Nanotechnol. 8, 522-6 (2013).-   10. Aghayee, S. et al. Combination of fluorescence microscopy and    nanomotion detection to characterize bacteria. J. Mol. Recognit. 26,    590-5 (2013).-   11. King, A. & King, A. those requiring special handling. 77-80    (2001).-   12. Gfeller, K. Y., Nugaeva, N. & Hegner, M. Rapid Biosensor for    Detection of Antibiotic-Selective Growth of Escherichia coli Rapid    Biosensor for Detection of Antibiotic-Selective Growth of    Escherichia coli. 71, (2005).-   13. Syal, K. W. W.; Sha. X.; Wan. S. C. H. Y. T. N. Plasmonic    imaging of protein interactions with single bacterial cells.-   14. Barenfanger, J., Drake, C. & Kacich, G. Clinical and financial    benefits of rapid bacterial identification and antimicrobial    susceptibility testing. J. Clin. Microbiol. 37, 1415-8 (1999).-   15. Wang, W. et al. kinetics of membrane proteins in single living    cells. 4, (2012).-   16. Wang, W. et al. Single cells and intracellular processes studied    by a plasmonic-based electrochemical impedance microscopy. Nat.    Chem. 3, 249-55(2011).-   17. Shan, X. et al. Imaging the electrocatalytic activity of single    nanoparticles. Nat. Nanotechnol. 7, 668-72 (2012).-   18. Wang, S. et al. Label-free imaging, detection, and mass    measurement of single viruses by surface plasmon resonance. Proc.    Natl. Acad. Sci. U.S.A. 107, 16028-32 (2010).-   19. Shan: X. N. Shan, Y. M. Fang, S. P. Wang, Y. Yuan, H.-Y. C.    and N. J. T. Detection of charges and molecules with self-assembled    nano-oscillators. Nano Lett. (2014).-   20. Wang, X., Meier, R. J. & Wolfbeis, O. S. Fluorescent    pH-sensitive nanoparticles in an agarose matrix for imaging of    bacterial growth and metabolism. Angew. Chem. Int. Ed. Engl. 52,    406-9 (2013).-   21. Chattopadhyay, S., Moldovan, R., Yeung, C. & Wu, X. L. Swimming    efficiency of bacterium Escherichia coli. Proc. Natl. Acad. Sci.    U.S.A. 103, 13712-7 (2006).-   22. Kuo, S. C. & Koshland, D. E. Roles of cheY and cheZ gene    products in controlling flagellar rotation in bacterial chemotaxis    of Escherichia coli. J. Bacteriol. 169, 1307-14 (1987).-   23. Weiss, L. E. et al. Engineering motility as a phenotypic    response to LuxI/R-dependent quorum sensing in Escherichia coli.    Biotechnol. Bioeng. 100, 1251-5 (2008).-   24. Mignot, T., Shaevitz, J. W., Hartzell, P. L. & Zusman, D. R.    Evidence that focal adhesion complexes power bacterial gliding    motility. Science 315, 853-6 (2007).-   25. Shi, W. & Lux, R. Focal adhesion: getting a grasp on    myxobacterial gliding Iron-sulfur clusters as oxygen-responsive. 3,    205-206 (2007).-   26. Topp, S. & Gallivan, J. P. Guiding bacteria with small molecules    and RNA. J. Am. Chem. Soc. 129, 6807-11(2007).-   27. Sochacki, K. a, Barns, K. J., Bucki, R. & Weisshaar, J. C.    Real-time attack on single Escherichia coli cells by the human    antimicrobial peptide LL-37. Proc. Natl. Acad. Sci. U.S.A. 108,    E77-81 (2011).-   28. Mohan, R. et al. A multiplexed microfluidic platform for rapid    antibiotic susceptibility testing. Biosens. Bioelectron. 49, 118-25    (2013).-   29. Butler, M. T., Wang, Q. & Harshey, R. M. Cell density and    mobility protect swarming bacteria against antibiotics. Proc. Natl.    Acad. Sci. U.S.A. 107, 3776-81 (2010).-   30. Linares, J. F., Gustafsson, I., Baquero, F. & Martinez, J. L.    Antibiotics as intermicrobial signaling agents. 103, (2006).-   31. Daniels, R. Surviving the First Hours in Sepsis: Getting the    Basics Right (an Intensivist's Perspective). jAntimicrob. Chemother.    2011, 66, 11-23.-   32. Wood, K. a.; Angus, D. C. Pharmacoeconomic Implications of New    Therapies in Sepsis. PharmacoEconomics 2004, 22, 895-906.-   33. Sivanandan, S.; Soraisham, A. S.; Swarnam, K. Choice and    Duration of Antimicrobial Therapy for Neonatal Sepsis and    Meningitis. Int. jPediatr. 2011, 2011, 1-9.-   34. Harbarth, S.; Garbino, J.; Pugin, J.; Romand, J. a.; Lew, D.;    Pittet, D. Inappropriate Initial Antimicrobial Therapy and Its    Effect on Survival in a Clinical Trial of Immunomodulating Therapy    for Severe Sepsis. Am. jMed. 2003, 115, 529-535.-   35. Angus, D. C.; van der Poll, T. Severe Sepsis and Septic    Shock. N. Engl. jMed. 2013, 369, 840-851.-   36. Centers for Disease and Control Prevention. Antibiotic    Resistance Threats in the United States, 2013; U.S. Department of    Heath and Human Services: Washington, D.C., 2013.-   37. Hancock, R. E. W. The End of an Era? Nat. Rev. Drug Discovery    2007, 6, 28-28.-   38. Jorgensen, J. H.; Ferraro, M. J. Antimicrobial Susceptibility    Testing: A Review of General Principles and Contemporary Practices.    Clin. Infect. Dis. 2009, 49, 1749-1755.-   39. Wiegand, I.; Hilpert, K.; Hancock, R. E. W. Agar and Broth    Dilution Methods to Determine the Minimal Inhibitory Concentration    (MIC) of Antimicrobial Substances. Nat. Protoc. 2008, 3, 163-175.-   40. Dalgaard, P.; Ross, T.; Kamperman, L.; Neumeyer, K.;    McMeekin, T. a. Estimation of Bacterial Growth Rates from    Turbidimetric and Viable Count Data. Int. jFood Microbiol. 1994, 23,    391-404.-   41. Sinn, I.; Albertson, T.; Kinnunen, P.; Breslauer, D. N.;    McNaughton, B. H.; Burns, M. a; Kopelman, R. Asynchronous Magnetic    Bead Rotation Microviscometer for Rapid, Sensitive, and Label-Free    Studies of Bacterial Growth and Drug Sensitivity. Anal. Chem. 2012,    84, 5250-5256.-   42. Kinnunen, P.; Sinn, I.; McNaughton, B. H.; Newton, D. W.;    Burns, M. a; Kopelman, R. Monitoring the Growth and Drug    Susceptibility of Individual Bacteria Using Asynchronous Magnetic    Bead Rotation Sensors. Biosens. Bioelectron. 2011, 26, 2751-2755.-   43. Price, C. S.; Kon, S. E.; Metzger, S. Rapid Antibiotic    Susceptibility Phenotypic Characterization of Staphylococcus Aureus    Using Automated Microscopy of Small Numbers of Cells. jMicrobiol.    Methods 2014, 98, 50-58.-   44. Choi, J.; Jung, Y.-G.; Kim, J.; Kim, S.; Jung, U.; Na, H.;    Kwon, S. Rapid Antibiotic Susceptibility Testing by Tracking Single    Cell Growth in a Microfluidic Agarose Channel System. Lab Chip 2013,    13, 280-287.-   45. Fredborg, M.; Andersen, K. R.; Jørgensen, E.; Droce, A.; Olesen,    T.; Jensen, B. B.; Rosenvinge, F. S.; Sondergaard, T. E. Real-Time    Optical Antimicrobial Susceptibility Testing. jClin. Microbiol.    2013, 51, 2047-2053.-   46. Mohan, R.; Mukherjee, A.; Sevgen, S. E.; Sanpitakseree, C.; Lee,    J.; Schroeder, C. M.; Kenis, P. J. a. A Multiplexed Microfluidic    Platform for Rapid Antibiotic Susceptibility Testing. Biosens.    Bioelectron. 2013, 49, 118-125.-   47. Lu, Y.; Gao, J.; Zhang, D. D.; Gau, V.; Liao, J. C.; Wong, P. K.    Single Cell Antimicrobial Susceptibility Testing by Confined    Micro-channels and Electrokinetic Loading. Anal. Chem. 2013, 85,    3971-3976.-   48. Wang, X.; Meier, R. J.; Wolfbeis, O. S. Fluorescent pH-Sensitive    Nanoparticles in an Agarose Matrix for Imaging of Bacterial Growth    and Metabolism. Angew. Chem., Int. Ed. 2013, 52, 406-409.-   49. Metzger, S.; Frobel, R. a.; Dunne, W. M. Rapid Simultaneous    Identification and Quantitation of Staphylococcus Aureus and    Pseudomonas Aeruginosa Directly from Bronchoalveolar Lavage    Specimens Using Automated Microscopy. Diagn. Microbiol. Infect. Dis.    2014, 79, 160-165.-   50. Choi, J.; Yoo, J.; Lee, M.; Kim, E.; Lee, J. S.; Lee, S.; Joo,    S.; Song, S. H.; Kim, E.; Lee, J. C.; et al. A Rapid Antimicrobial    Susceptibility Test Based on Single-Cell Morphological Analysis.    Sci. Transl. Med. 2014, 6, 267ra174.-   51. King, A. Recommendations for Susceptibility Tests on Fastidious    Organisms and Those Requiring Special Handling. J. Antimicrob.    Chemother. 2001, 48, 77-80.-   52. Longo, G.; Alonso-Sarduy, L.; Rio, L. M.; Bizzini, a; Trampuz,    a; Notz, J.; Dietler, G.; Kasas, S. Rapid Detection of Bacterial    Resistance to Antibiotics Using AFM Cantilevers as Nanomechanical    Sensors. Nat. Nanotechnol. 2013, 8, 522-526.-   53. Aghayee, S.; Benadiba, C.; Notz, J.; Kasas, S.; Dietler, G.;    Longo, G. Combination of Fluorescence Microscopy and Nanomotion    Detection to Characterize Bacteria. J. Mol. Recognit. 2013, 26,    590-595.-   54. Lissandrello, C.; Inci, F.; Francom, M.; Paul, M. R.; Demirci,    U.; Ekinci, K. L. Nanomechanical Motion of Escherichia Coli Adhered    to a Surface. Appl. Phys. Lett. 2014, 105, 113701.-   55. Kasas, S.; Simone, F.; Benadiba, C.; Maillard, C.; Stupar, P.;    Tournu, H. Detecting Nanoscale Vibrations as Signature of Life.    Proc. Natl. Acad. Sci. U.S.A. 2015, 112, 378-381.-   56. Song, L.; Sjollema, J.; Sharma, P. K.; Kaper, H. J.; van der    Mei, H. C.; Busscher, H. J. Nanoscopic Vibrations of Bacteria with    Different Cell-Wall Properties Adhering to Surfaces under Flow and    Static Conditions. ACS Nano 2014, 8, 8457-8467.-   57. Syal, K.; Wang, W.; Shan, X.; Wang, S.; Chen, H. Y.; Tao, N.    Plasmonic Imaging of Protein Interactions with Single Bacterial    Cells. Biosens. Bioelectron. 2015, 63, 131-137.-   58. Wang, W.; Foley, K.; Shan, X.; Wang, S.; Eaton, S.; Nagaraj, V.    J.; Wiktor, P.; Patel, U.; Tao, N. Single Cells and Intracellular    Processes Studied by a Plasmonic-Based Electrochemical Impedance    Microscopy. Nat. Chem. 2011, 3, 249-255.-   59. Besser, R. E.; Griffin, P. M.; Slutsker, L. Escherichia Coli    O157:H7 Gastroenteritis and the Hemolytic Uremic Syndrome: An    Emerging Infectious Disease. Annu. Rev. Med. 1999, 50, 355-367.-   60. Daugelavičius, R.; Bakiené, E.; Bamford, D. H. Stages of    Polymyxin B Interaction with the Escherichia Coli Cell Envelope.    Antimicrob. Agents Chemother. 2000, 44, 2969-2978.-   61. Flores-Mireles, A. L.; Walker, J. N.; Caparon, M.;    Hultgren, S. J. Urinary Tract Infections: Epidemiology, Mechanisms    of Infection and Treatment Options. Nat. Rev. Microbiol. 2015, 13,    269-284.-   62. Wang, W.; Yang, Y.; Wang, S.; Nagaraj, V. J.; Liu, Q.; Wu, J.;    Tao, N. Label-Free Measuring and Mapping of Binding Kinetics of    Membrane Proteins in Single Living Cells. Nat. Chem. 2012, 4,    846-873.-   63. Wang, S.; Shan, X.; Patel, U.; Huang, X.; Lu, J.; Li, J.;    Tao, N. Label-Free Imaging, Detection, and Mass Measurement of    Single Viruses by Surface Plasmon Resonance. Proc. Natl. Acad. Sci.    U.S.A. 2010, 107, 16028-16032.-   64. Shan, X.; DíeZ-Pérez, I.; Wang, L.; Wiktor, P.; Gu, Y.; Zhang,    L.; Wang, W.; Lu, J.; Wang, S.; Gong, Q.; et al. Imaging the    Electrocatalytic Activity of Single Nanoparticles. Nat. Nanotechnol.    2012, 7, 668-672.-   65. Yang, Y.; Yu, H.; Shan, X.; Wang, W.; Liu, X.; Wang, S.; Tao, N.    Label-Free Tracking of Single Organelle Transportation in Cells with    Nanometer Precision Using a Plasmonic Imaging Technique. Small 2015,    11, 2878-2884.-   66. Shan, X.; Fang, Y.; Wang, S.; Guan, Y.; Chen, H. Y.; Tao, N.    Detection of Charges and Molecules with Self-Assembled    Nano-Oscillators. Nano Lett. 2014, 14, 4151-4157.-   67. Parry, B. R.; Surovtsev, I. V.; Cabeen, M. T.; O'Hern, C. S.;    Dufresne, E. R.; Jacobs-Wagner, C. The Bacterial Cytoplasm Has    Glass-like Properties and Is Fluidized by Metabolic Activity. Cell    2014, 156, 183-194.-   68. Zhang, Y.; Rock, C. O. Membrane Lipid Homeostasis in Bacteria.    Nat. Rev. Microbiol. 2008, 6, 222-233.-   69. Sochacki, K. a; Barns, K. J.; Bucki, R.; Weisshaar, J. C.    Real-Time Attack on Single Escherichia Coli Cells by the Human    Antimicrobial Peptide LL-37. Proc. Natl. Acad. Sci. U.S.A. 2011,    108, E77-E81.-   70. Ocampo, P. S.; Lázár, V.; Papp, B.; Arnoldini, M.; Zur    Wiesch, P. A.; Busa-Fekete, R.; Fekete, G.; Pál, C.; Ackermann, M.;    Bonhoeffer, S. Antagonism between Bacteriostatic and Bactericidal    Antibiotics Is Prevalent. Antimicrob. Agents Chemother. 2014, 58,    4573-4582.

What is claimed is:
 1. A method for antibiotic susceptibility testingusing plasmonic imaging for bacterial cells comprising: providing aplasmonic imaging and tracking (PIT) system including an invertedmicroscope lens, a light source, a metallic coated slide, a mirror and adetector; attaching tethering molecules to the metallic coated surface;populating the metallic coated surface with bacteria; activating the PITsystem; imaging the bacteria using the PIT system; tracking a first setof 3D motion values of the bacteria; adding an antibiotic to themetallic coated surface; tracking a second set of 3D motion values ofthe bacteria in the presence of the antibiotic; and comparing the firstand second 3D motion values to determine changes in the 3D motion of thebacteria after addition of the antibiotic.
 2. The method of claim 1wherein attaching tethering molecules comprises attaching tetheringmolecules with an affinity to a bacterial cell under investigation. 3.The method of claim 2 wherein attaching tethering molecules comprisesattaching antibodies.
 4. The method of claim 1 wherein populating themetallic coated surface with a bacteria comprises populating themetallic coated surface with a bacteria selected from the groupconsisting of E. coli and S. aureus.
 5. The method of claim 1 whereinattaching tethering molecules comprises attaching tethering moleculesincluding anti-E. coli antibodies.
 6. The method of claim 1 whereinattaching tethering molecules comprises attaching tethering moleculesselected from the group consisting of cell-adhesion promotingsubstances, poly-lysine, and agar matrix.
 7. The method of claim 1wherein populating the metallic coated surface with the bacteriacomprises tethering the bacteria within a distance of less than fivehundred nm from the metallic coated surface.
 8. The method of claim 1wherein tracking a first set of 3D motion values comprises: extractingan image intensity change from the plasmonic image that is free ofnoise; processing a plasmonic image of a bacterial cell where theplasmonic image includes a bright spot with a parabolic shaped tail;tracking bacteria XY-motion by detecting the bright spot at the vertexof the parabolic shaped tail; and tracking bacteria Z-motion bydetecting substantially perpendicular motion relative to the metalliccoated surface.
 9. The method of claim 8 wherein tracking the XY-motioncomprises using a curve fitting algorithm.
 10. The method of claim 9wherein the curve-fitting algorithm is selected from the groupconsisting of Gaussian fitting, elliptical fitting, and spatialaveraging.
 11. The method of claim 8 wherein extracting an imageintensity change from the plasmonic image that is free of noisecomprises transforming the plasmonic image into K-space using Fouriertransforms to produce a two-ring image.
 12. A method for antibioticsusceptibility testing using plasmonic imaging for individual bacterialcells comprising: providing a plasmonic imaging and tracking (PIT)system including an inverted microscope lens, a light source, a metalliccoated slide, a mirror and a detector; attaching tethering molecules tothe metallic coated surface; populating the metallic coated surface withindividual bacterial cells; activating the PIT system; imaging, usingthe PIT system, the individual bacterial cells; tracking a first set of3D motion values of each of the individual bacterial cells; adding anantibiotic to the metallic coated surface; tracking a second set of 3Dmotion values of each of the individual bacterial cells in the presenceof the antibiotic; and comparing the first and second 3D motion valuesto determine changes in the 3D motion of each of the individualbacterial cells after addition of the antibiotic.